Network Control of Vehicle Lateral Dynamics With Control Allocation and Dynamic Message Priority Assignment

2013 ◽  
Author(s):  
Zhibin Shuai ◽  
Hui Zhang ◽  
Junmin Wang ◽  
Jianqiu Li ◽  
Minggao Ouyang
Keyword(s):  
Motion Control ◽  
Time Delays ◽  
Network Control ◽  
Lateral Motion ◽  
Priority Assignment ◽  
Limited Bandwidth ◽  
Vehicle Networks ◽  
Adverse Influence ◽  
Point To Point ◽  
Vehicle Network

In this paper we study the lateral motion control and torque allocation for four-wheel-independent-drive electric vehicles (4WID-EVs) with combined active front steering (AFS) and direct yaw moment control (DYC) through in-vehicle networks. It is well known that the in-vehicle networks and x-by-wire technologies have considerable advantages over the traditional point-to-point communications, and bring great strengths to 4WID-EVs. However, there are also bandwidth limitations which would lead to message time delays in network communication. We propose a method on effectively utilizing the limited bandwidth resources and attenuating the adverse influence of in-vehicle network-induced time delays, based on the idea of dynamic message priority assignment according to the vehicle states and control signals. Simulation results from a high-fidelity vehicle model in CarSim® show that the proposed vehicle lateral control and torque allocation algorithm can improve the 4WID-EV lateral motion control performance, and the proposed message priority dynamic assignment algorithm can significantly reduce the adverse influence of the in-vehicle network-induced time delays.

Coordinated Longitudinal and Lateral Motion Control for Four Wheel Independent Motor-Drive Electric Vehicle

2018 ◽  
Vol 67 (5) ◽  
pp. 3782-3790 ◽  
Author(s):  
Hongliang Zhou ◽  
Fengjiao Jia ◽  
Houhua Jing ◽  
Zhiyuan Liu ◽  
Levent Guvenc
Keyword(s):  
Electric Vehicle ◽  
Motion Control ◽  
Motor Drive ◽  
Lateral Motion

Command-filter-adaptive-based lateral motion control for autonomous vehicle

2022 ◽  
Vol 121 ◽  
pp. 105044
Author(s):  
Junda Zhang ◽  
Jian Wu ◽  
Jianmin Liu ◽  
Qing Zhou ◽  
Jianwei Xia ◽  
...  
Keyword(s):  
Motion Control ◽  
Autonomous Vehicle ◽  
Lateral Motion ◽  
Command Filter

Lateral Motion Control of a Vehicle Using the Rear Steer: Simulation Study

2015 ◽  
Author(s):  
Jin-Woo Lee ◽  
Bakhtiar B. Litkouhi
Keyword(s):  
Motion Control ◽  
Steering System ◽  
Lateral Motion ◽  
Automated Driving ◽  
Motion Error ◽  
Driving Mode ◽  
Vehicle Technology ◽  
Path Prediction ◽  
Future Work ◽  
Control Methodology

The lateral motion control is a key element for automated driving vehicle technology. Typically, the front steering system has been used as the primary actuator for vehicle lateral motion control. Alternatively, this paper presents a new method of the lateral motion control using a rear steer. When combined with the front steer actuator, the rear steer can generate more dynamically responsive turning of the vehicle. In addition, the rear steer can be used as a secondary back up actuator when the front steer actuator fails to operate during automated driving mode. Similar to the prior research that has used the front steer actuator for the lateral control, the control methodology presented in this paper maintains the same hierarchical framework, i.e., sensor fusion, path prediction, path planning, and motion control. Since the rear steer is in play for the vehicle lateral motion control, the equations for the path prediction and vehicle dynamics are re-derived with non-zero front steer and rear steer angles. Combined with the rear steering dynamics, the model predictive control (MPC) technique is applied for motion error minimization. This paper describes the theoretical part of the algorithm, and provides simulation results to show effectiveness of the algorithm. Future work will include vehicle implementation, testing, and evaluation.

Implementation of Intelligent Control System for Autonomous Underwater Vehicle

2014 ◽  
Vol 701-702 ◽  
pp. 704-710 ◽  
Author(s):  
Viacheslav Pshikhopov ◽  
Yuriy Chernukhin ◽  
Viktor Guzik ◽  
Mikhail Medvedev ◽  
Boris Gurenko ◽  
...  
Keyword(s):  
Control System ◽  
Motion Control ◽  
Intelligent Control ◽  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Underwater Vehicle ◽  
Intelligent Control System ◽  
Azov Sea ◽  
Complex Simulation ◽  
Point To Point

This paper introduces the implementation of intelligent motion control and planning for autonomous underwater vehicle (AUV). Previously developed control system features intelligent motion control and planning subsystem, based on artificial neural networks. It allows detecting and avoiding moving obstacles in front of the AUV. The motion control subsystem uses position-trajectory control method to position AUV, move from point to point and along given path with given speed. Control system was tested in the multi-module simulation complex. Simulation showed good results – AUV successfully achieved given goals avoiding collisions not only with static obstacles, but also with mobile ones. That allows using the proposed control system for the groups of vehicles. Besides simulation, control system was implemented in hardware. AUV prototype passed tests in Azov Sea and proved its efficiency.

The Application of Command Shaping to the Tracking Problem

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Michael C. Reynolds ◽  
Peter H. Meckl
Keyword(s):  
Control Problem ◽  
Motion Control ◽  
Tracking Error ◽  
Tracking Problem ◽  
Command Shaping ◽  
Optimal Tracking ◽  
Tracking Technique ◽  
Optimal Input ◽  
Point Motion ◽  
Point To Point

This work presents a novel technique for the solution of an optimal input for trajectory tracking. Many researchers have documented the performance advantages of command shaping, which focuses on the design of an optimal input. Nearly all research in command shaping has been centered on the point-to-point motion control problem. However, tracking problems are also an important application of control theory. The proposed optimal tracking technique extends the point-to-point motion control problem to the solution of the tracking problem. Thus, two very different problems are brought into one solution scheme. The technique uses tolerances on trajectory following to meet constraints and minimize either maneuver time or input energy. A major advantage of the technique is that hard physical constraints such as acceleration or allowable tracking error can be directly constrained. Previous methods to perform such a task involved using various weightings that lack physical meaning. The optimal tracking technique allows for fast and efficient exploration of the solution space for motion control. A solution verification technique is presented and some examples are included to demonstrate the technique.

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